Table of contents

  1. Front Matter
    Pages I-XII
  2. Invited Papers

  3. Research Papers

    1. Joachim Baumeister, Martin Atzmüller, Frank Puppe
      Pages 28-42
    2. James D. Carswell, David C. Wilson, Michela Bertolotto
      Pages 58-72
    3. Belén Díaz-Agudo, Pablo Gervás, Pedro A. González-Calero
      Pages 73-87
    4. Göran Falkman
      Pages 88-102
    5. Paulo Gomes, Francisco C. Pereira, Paulo Paiva, Nuno Seco, Paulo Carreiro, José L. Ferreira et al.
      Pages 118-132
    6. Kalyan Moy Gupta, David W. Aha, Nabil Sandhu
      Pages 133-147
    7. Daniel N. Hennessy, Bruce G. Buchanan, John M. Rosenberg
      Pages 148-158
    8. Martha Dørum Jære, Agnar Aamodt, Pål Skalle
      Pages 174-188
    9. Boris Kerkez, Michael T. Cox
      Pages 189-203
    10. David B. Leake, Raja Sooriamurthi
      Pages 204-218
    11. David McSherry
      Pages 219-233
    12. Miquel Montaner, Beatriz López, Josep Lluís de la Rosa
      Pages 234-248
    13. Babak Mougouie, Ralph Bergmann
      Pages 249-263

About these proceedings

Introduction

The papers collected in this volume were presented at the 6th European C- ference on Case-Based Reasoning (ECCBR 2002) held at The Robert Gordon University in Aberdeen, UK. This conference followed a series of very succe- ful well-established biennial European workshops held in Trento, Italy (2000), Dublin, Ireland (1998), Lausanne, Switzerland (1996), and Paris, France (1994), after the initial workshop in Kaiserslautern, Germany (1993). These meetings have a history of attracting ?rst-class European and international researchers and practitioners in the years interleaving with the biennial international co- terpart ICCBR; the 4th ICCBR Conference was held in Vancouver, Canada in 2001. Proceedings of ECCBR and ICCBR conferences are traditionally published by Springer-Verlag in their LNAI series. Case-Based Reasoning (CBR) is an AI problem-solving approach where pr- lems are solved by retrieving and reusing solutions from similar, previously solved problems, and possibly revising the retrieved solution to re?ect di?erences - tween the new and retrieved problems. Case knowledge stores the previously solved problems and is the main knowledge source of a CBR system. A main focus of CBR research is the representation, acquisition and maintenance of case knowledge. Recently other knowledge sources have been recognized as important: indexing, similarity and adaptation knowledge. Signi?cant knowledge engine- ing e?ort may be needed for these, and so the representation, acquisition and maintenance of CBR knowledge more generally have become important.

Keywords

AI Planning CBR Case-Based Making Case-Based Management Case-Based Problem Solving Case-Based Reasoning Case-Based Reasoning Systems, Fuzzy Knowledge Engineering Optimization Methods cognition complexity organization proving recommender system

Editors and affiliations

  • Susan Craw
    • 1
  • Alun Preece
    • 2
  1. 1.School of ComputingThe Robert Gordon UniversityAberdeenScotland, UK
  2. 2.Computing Science DepartmentUniversity of AberdeenAberdeenScotland, UK

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-46119-1
  • Copyright Information Springer-Verlag Berlin Heidelberg 2002
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-44109-0
  • Online ISBN 978-3-540-46119-7
  • Series Print ISSN 0302-9743
  • About this book